# !/usr/bin/env python3
# Adaptation of the following sources:
# https://github.com/bioinf-jku/TTUR/fid.py commit id d4baae8
import json
import os
import pathlib
import sys
import tempfile
import warnings
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter

import numpy as np
import tensorflow as tf
from imageio import imread
from scipy import linalg
from tfdeterminism import patch as patch_tensorflow_for_determinism
from tqdm import tqdm

KEY_FID = 'frechet_inception_distance'


def create_inception_graph(pth):
    """Creates a graph from saved GraphDef file."""
    # Creates graph from saved graph_def.pb.
    with tf.io.gfile.GFile(pth, 'rb') as f:
        graph_def = tf.compat.v1.GraphDef()
        graph_def.ParseFromString(f.read())
        _ = tf.import_graph_def(graph_def, name='FID_Inception_Net')


# code for handling inception net derived from
#   https://github.com/openai/improved-gan/blob/master/inception_score/model.py
def get_inception_layer(sess):
    """Prepares inception net for batched usage and returns pool_3 layer. """
    layername = 'FID_Inception_Net/pool_3:0'
    pool3 = sess.graph.get_tensor_by_name(layername)
    ops = pool3.graph.get_operations()
    for op_idx, op in enumerate(ops):
        for o in op.outputs:
            shape = o.get_shape()
            if shape._dims is not None:
                # shape = [s.value for s in shape] TF 1.x
                shape = [s for s in shape]  # TF 2.x
                new_shape = []
                for j, s in enumerate(shape):
                    if s == 1 and j == 0:
                        new_shape.append(None)
                    else:
                        new_shape.append(s)
                o.__dict__['_shape_val'] = tf.TensorShape(new_shape)
    return pool3


def get_activations(images, sess, batch_size=50, verbose=False):
    """Calculates the activations of the pool_3 layer for all images.

    Params:
    -- images      : Numpy array of dimension (n_images, hi, wi, 3). The values
                     must lie between 0 and 256.
    -- sess        : current session
    -- batch_size  : the images numpy array is split into batches with batch size
                     batch_size. A reasonable batch size depends on the disposable hardware.
    -- verbose    : If set to True and parameter out_step is given, the number of calculated
                     batches is reported.
    Returns:
    -- A numpy array of dimension (num images, 2048) that contains the
       activations of the given tensor when feeding inception with the query tensor.
    """
    inception_layer = get_inception_layer(sess)
    n_images = images.shape[0]
    if batch_size > n_images:
        if verbose:
            print("WARNING: batch size is bigger than the data size. setting batch size to data size", sys.stderr)
        batch_size = n_images
    n_batches = n_images // batch_size
    pred_arr = np.empty((n_images, 2048))
    for i in tqdm(range(n_batches), disable=not verbose):
        start = i * batch_size
        if start + batch_size < n_images:
            end = start + batch_size
        else:
            end = n_images
        batch = images[start:end]
        pred = sess.run(inception_layer, {'FID_Inception_Net/ExpandDims:0': batch})
        pred_arr[start:end] = pred.reshape(batch_size, -1)
    return pred_arr


def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
    """Numpy implementation of the Frechet Distance.
    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
    and X_2 ~ N(mu_2, C_2) is
            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).

    Stable version by Dougal J. Sutherland.

    Params:
    -- mu1 : Numpy array containing the activations of the pool_3 layer of the
             inception net ( like returned by the function 'get_predictions')
             for generated samples.
    -- mu2   : The sample mean over activations of the pool_3 layer, precalcualted
               on an representive data set.
    -- sigma1: The covariance matrix over activations of the pool_3 layer for
               generated samples.
    -- sigma2: The covariance matrix over activations of the pool_3 layer,
               precalcualted on an representive data set.

    Returns:
    --   : The Frechet Distance.
    """

    mu1 = np.atleast_1d(mu1)
    mu2 = np.atleast_1d(mu2)

    sigma1 = np.atleast_2d(sigma1)
    sigma2 = np.atleast_2d(sigma2)

    assert mu1.shape == mu2.shape, "Training and test mean vectors have different lengths"
    assert sigma1.shape == sigma2.shape, "Training and test covariances have different dimensions"

    diff = mu1 - mu2

    # product might be almost singular
    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
    if not np.isfinite(covmean).all():
        msg = "fid calculation produces singular product; adding %s to diagonal of cov estimates" % eps
        warnings.warn(msg)
        offset = np.eye(sigma1.shape[0]) * eps
        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))

    # numerical error might give slight imaginary component
    if np.iscomplexobj(covmean):
        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
            m = np.max(np.abs(covmean.imag))
            raise ValueError("Imaginary component {}".format(m))
        covmean = covmean.real

    tr_covmean = np.trace(covmean)

    return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean


def calculate_activation_statistics(images, sess, batch_size=50, verbose=False):
    """Calculation of the statistics used by the FID.
    Params:
    -- images      : Numpy array of dimension (n_images, hi, wi, 3). The values
                     must lie between 0 and 255.
    -- sess        : current session
    -- batch_size  : the images numpy array is split into batches with batch size
                     batch_size. A reasonable batch size depends on the available hardware.
    -- verbose     : If set to True and parameter out_step is given, the number of calculated
                     batches is reported.
    Returns:
    -- mu    : The mean over samples of the activations of the pool_3 layer of
               the incption model.
    -- sigma : The covariance matrix of the activations of the pool_3 layer of
               the incption model.
    """
    act = get_activations(images, sess, batch_size=batch_size, verbose=verbose)
    mu = np.mean(act, axis=0)
    sigma = np.cov(act, rowvar=False)
    return mu, sigma


# ------------------
# The following methods are implemented to obtain a batched version of the activations.
# This has the advantage to reduce memory requirements, at the cost of slightly reduced efficiency.
# - Pyrestone
# ------------------


def load_image_batch(files):
    """Convenience method for batch-loading images
    Params:
    -- files    : list of paths to image files. Images need to have same dimensions for all files.
    Returns:
    -- A numpy array of dimensions (num_images,hi, wi, 3) representing the image pixel values.
    """
    return np.array([imread(str(fn)).astype(np.float32) for fn in files])


def get_activations_from_files(files, sess, batch_size=50, verbose=False):
    """Calculates the activations of the pool_3 layer for all images.

    Params:
    -- files      : list of paths to image files. Images need to have same dimensions for all files.
    -- sess        : current session
    -- batch_size  : the images numpy array is split into batches with batch size
                     batch_size. A reasonable batch size depends on the disposable hardware.
    -- verbose    : If set to True and parameter out_step is given, the number of calculated
                     batches is reported.
    Returns:
    -- A numpy array of dimension (num images, 2048) that contains the
       activations of the given tensor when feeding inception with the query tensor.
    """
    inception_layer = get_inception_layer(sess)
    n_imgs = len(files)
    if batch_size > n_imgs:
        if verbose:
            print("WARNING: batch size is bigger than the data size. setting batch size to data size", sys.stderr)
        batch_size = n_imgs
    n_batches = n_imgs // batch_size + 1
    pred_arr = np.empty((n_imgs, 2048))
    for i in tqdm(range(n_batches), disable=not verbose):
        start = i * batch_size
        if start + batch_size < n_imgs:
            end = start + batch_size
        else:
            end = n_imgs
        batch = load_image_batch(files[start:end])
        pred = sess.run(inception_layer, {'FID_Inception_Net/ExpandDims:0': batch})
        pred_arr[start:end] = pred.reshape(batch_size, -1)
        del batch  # clean up memory
    return pred_arr


def calculate_activation_statistics_from_files(files, sess, batch_size=50, verbose=False):
    """Calculation of the statistics used by the FID.
    Params:
    -- files      : list of paths to image files. Images need to have same dimensions for all files.
    -- sess        : current session
    -- batch_size  : the images numpy array is split into batches with batch size
                     batch_size. A reasonable batch size depends on the available hardware.
    -- verbose     : If set to True and parameter out_step is given, the number of calculated
                     batches is reported.
    Returns:
    -- mu    : The mean over samples of the activations of the pool_3 layer of
               the incption model.
    -- sigma : The covariance matrix of the activations of the pool_3 layer of
               the incption model.
    """
    act = get_activations_from_files(files, sess, batch_size=batch_size, verbose=verbose)
    mu = np.mean(act, axis=0)
    sigma = np.cov(act, rowvar=False)
    return mu, sigma


# -------------------------------------------------------------------------------
# The following functions aren't needed for calculating the FID
# they're just here to make this module work as a stand-alone script
# for calculating FID scores
# -------------------------------------------------------------------------------
def check_or_download_inception(verbose=False):
    ''' Checks if the path to the inception file is valid, or downloads
        the file if it is not present. '''
    INCEPTION_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
    inception_path = tempfile.gettempdir()
    inception_path = pathlib.Path(inception_path)
    model_file = inception_path / 'classify_image_graph_def.pb'
    if not model_file.exists():
        if verbose:
            print("Downloading Inception model", sys.stderr)
        from urllib import request
        import tarfile
        fn, _ = request.urlretrieve(INCEPTION_URL)
        with tarfile.open(fn, mode='r') as f:
            f.extract('classify_image_graph_def.pb', str(model_file.parent))
    return str(model_file)


def handle_path(path, sess, low_profile=False, verbose=False):
    if path.endswith('.npz'):
        f = np.load(path)
        m, s = f['mu'][:], f['sigma'][:]
        f.close()
    else:
        path = pathlib.Path(path)
        files = list(path.glob('*.jpg')) + list(path.glob('*.png'))
        if low_profile:
            m, s = calculate_activation_statistics_from_files(files, sess, verbose=verbose)
        else:
            x = np.array([imread(str(fn)).astype(np.float32) for fn in files])
            m, s = calculate_activation_statistics(x, sess, verbose=verbose)
            del x  # clean up memory
    return m, s


def calculate_fid_given_paths(paths, low_profile=False, verbose=False):
    ''' Calculates the FID of two paths. '''
    inception_path = check_or_download_inception(verbose=verbose)

    for p in paths:
        if not os.path.exists(p):
            raise RuntimeError("Invalid path: %s" % p)

    create_inception_graph(str(inception_path))
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        m1, s1 = handle_path(paths[0], sess, low_profile=low_profile, verbose=verbose)
        m2, s2 = handle_path(paths[1], sess, low_profile=low_profile, verbose=verbose)
        fid_value = calculate_frechet_distance(m1, s1, m2, s2)
    return {
        KEY_FID: float(fid_value)
    }


if __name__ == "__main__":
    parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
    parser.add_argument("path", type=str, nargs=2,
                        help='Path to the generated images or to .npz statistic files')
    parser.add_argument("--gpu", default='', type=str,
                        help='GPU to use (leave blank for CPU only)')
    parser.add_argument('--json', action='store_true',
                        help='Print scores in JSON')
    parser.add_argument('--determinism', action='store_true',
                        help='Enforce determinism in TensorFlow to remove variance when running with the same inputs. '
                             'Without it inception score varies between different runs on the same data (e.g. 18.11 +/- '
                             '0.02). More information: https://github.com/NVIDIA/tensorflow-determinism')
    parser.add_argument("--lowprofile", action="store_true",
                        help='Keep only one batch of images in memory at a time. '
                             'This reduces memory footprint, but may decrease speed slightly.')
    parser.add_argument('-s', '--silent', action='store_true',
                        help='Verbose or silent progress bar and messages')
    args = parser.parse_args()

    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu

    if args.determinism:
        patch_tensorflow_for_determinism()

    metrics = calculate_fid_given_paths(args.path, low_profile=args.lowprofile, verbose=not args.silent)

    if args.json:
        print(json.dumps(metrics, indent=4))
    else:
        print(', '.join((f'{k}: {v}' for k, v in metrics.items())))
